OpenRaiser/Intern-Atlas huggingface dataset:
It was the training data pruning too!
Venue: arXiv cs.LG 2018
Year: 2018

We study the current best model (KDG) for question answering on tabular data evaluated over the WikiTableQuestions dataset. Previous ablation studies performed against this model attributed the model's performance to certain aspects of its architecture. In this paper, we find that the model's performance also crucially depends on a certain pruning of the data used to train the model. Disabling the pruning step drops the accuracy of the model from 43.3% to 36.3%. The large impact on the performance of the KDG model suggests that the pruning may be a useful pre-processing step in training other semantic parsers as well.
Correct Undetected Errors with List Decoding in ARQ Error-control Systems
Venue: arXiv cs.IT 2018
Year: 2018

Undetected errors are important for linear codes, which are the only type of errors after hard decision and automatic-repeat-request (ARQ), but do not receive much attention on their correction. In concatenated channel coding, suboptimal source coding and joint source-channel coding, constrains among successive codewords may be utilized to improve decoding performance. In this paper, list decoding is used to correct the undetected errors. The benefit proportion of the correction is obviously improved especially on Hamming codes and Reed-Muller codes, which achieves about 40% in some cases. But this improvement is significant only after the selection of final codewords from the lists based on the constrains among the successive transmitted codewords. The selection algorithm is investigated here to complete the list decoding program in the application of Markov context model. The performance of the algorithm is analysed and a lower bound of the correctly selected probability is derived to determine the proper context length.
Compressive Sensing Based User Clustering for Downlink NOMA Systems with Decoding Power
Venue: arXiv cs.IT 2018
Year: 2018

This letter investigates joint power control and user clustering for downlink non-orthogonal multiple access systems. Our aim is to minimize the total power consumption by taking into account not only the conventional transmission power but also the decoding power of the users. To solve this optimization problem, it is firstly transformed into an equivalent problem with tractable constraints. Then, an efficient algorithm is proposed to tackle the equivalent problem by using the techniques of reweighted 1-norm minimization and majorization-minimization. Numerical results validate the superiority of the proposed algorithm over the conventional algorithms including the popular matching-based algorithm.
Dynamic Vision Sensors for Human Activity Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Unlike conventional cameras which capture video at a fixed frame rate, Dynamic Vision Sensors (DVS) record only changes in pixel intensity values. The output of DVS is simply a stream of discrete ON/OFF events based on the polarity of change in its pixel values. DVS has many attractive features such as low power consumption, high temporal resolution, high dynamic range and fewer storage requirements. All these make DVS a very promising camera for potential applications in wearable platforms where power consumption is a major concern. In this paper, we explore the feasibility of using DVS for Human Activity Recognition (HAR). We propose to use the various slices (such as $x-y$, $x-t$, and $y-t$) of the DVS video as a feature map for HAR and denote them as Motion Maps. We show that fusing motion maps with Motion Boundary Histogram (MBH) give good performance on the benchmark DVS dataset as well as on a real DVS gesture dataset collected by us. Interestingly, the performance of DVS is comparable to that of conventional videos although DVS captures only sparse motion information.
Invisible Mask: Practical Attacks on Face Recognition with Infrared
Venue: arXiv cs.CR 2018
Year: 2018

Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross boarder travelers could pass a face authentication inspection line without the involvement of officers. Nonetheless, when public security heavily relies on such intelligent systems, the designers should deliberately consider the emerging attacks aiming at misleading those systems employing face recognition. We propose a kind of brand new attack against face recognition systems, which is realized by illuminating the subject using infrared according to the adversarial examples worked out by our algorithm, thus face recognition systems can be bypassed or misled while simultaneously the infrared perturbations cannot be observed by raw eyes. Through launching this kind of attack, an attacker not only can dodge surveillance cameras. More importantly, he can impersonate his target victim and pass the face authentication system, if only the victim's photo is acquired by the attacker. Again, the attack is totally unobservable by nearby people, because not only the light is invisible, but also the device we made to launch the attack is small enough. According to our study on a large dataset, attackers have a very high success rate with a over 70\% success rate for finding such an adversarial example that can be implemented by infrared. To the best of our knowledge, our work is the first one to shed light on the severity of threat resulted from infrared adversarial examples against face recognition.
Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code
Venue: arXiv cs.LG 2018
Year: 2018

Translating a program written in one programming language to another can be useful for software development tasks that need functionality implementations in different languages. Although past studies have considered this problem, they may be either specific to the language grammars, or specific to certain kinds of code elements (e.g., tokens, phrases, API uses). This paper proposes a new approach to automatically learn cross-language representations for various kinds of structural code elements that may be used for program translation. Our key idea is two folded: First, we normalize and enrich code token streams with additional structural and semantic information, and train cross-language vector representations for the tokens (a.k.a. shared embeddings based on word2vec, a neural-network-based technique for producing word embeddings; Second, hierarchically from bottom up, we construct shared embeddings for code elements of higher levels of granularity (e.g., expressions, statements, methods) from the embeddings for their constituents, and then build mappings among code elements across languages based on similarities among embeddings. Our preliminary evaluations on about 40,000 Java and C# source files from 9 software projects show that our approach can automatically learn shared embeddings for various code elements in different languages and identify their cross-language mappings with reasonable Mean Average Precision scores. When compared with an existing tool for mapping library API methods, our approach identifies many more mappings accurately. The mapping results and code can be accessed at https://github.com/bdqnghi/hierarchical-programming-language-mapping. We believe that our idea for learning cross-language vector representations with code structural information can be a useful step towards automated program translation.
Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features
Venue: arXiv cs.CV 2018
Year: 2018

Robust features are of vital importance to face spoofing detection, because various situations make feature space extremely complicated to partition. Thus in this paper, two novel and robust features for anti-spoofing are proposed. The first one is a binocular camera based depth feature called Template Face Matched Binocular Depth (TFBD) feature. The second one is a high-level micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT) feature. Novel template face registration algorithm and spatial pyramid coding algorithm are also introduced along with the two novel features. Multi-modal face spoofing detection is implemented based on these two robust features. Experiments are conducted on a widely used dataset and a comprehensive dataset constructed by ourselves. The results reveal that face spoofing detection with the fusion of our proposed features is of strong robustness and time efficiency, meanwhile outperforming other state-of-the-art traditional methods.
Storage and Repair Bandwidth Tradeoff for Distributed Storage Systems with Clusters and Separate Nodes
Venue: arXiv cs.DC 2018
Year: 2018

The optimal tradeoff between node storage and repair bandwidth is an important issue for distributed storage systems (DSSs). As for realistic DSSs with clusters, when repairing a failed node, it is more efficient to download more data from intra-cluster nodes than from cross-cluster nodes. Therefore, it is meaningful to differentiate the repair bandwidth from intra-cluster and cross-cluster. For cluster DSSs the tradeoff has been considered with special repair assumptions where all the alive nodes are utilized to repair a failed node. In this paper, we investigate the optimal tradeoff for cluster DSSs under more general storage/repair parameters. Furthermore, a regenerating code construction strategy achieving the points in the optimal tradeoff curve is proposed for cluster DSSs with specific parameters as a numerical example. Moreover, the influence of separate nodes for the tradeoff is also considered for DSSs with clusters and separated nodes.
Solving First Order Autonomous Algebraic Ordinary Differential Equations by Places
Venue: arXiv cs.SC 2018
Year: 2018

Given a first-order autonomous algebraic ordinary differential equation, we present a method for computing formal power series solutions by means of places. We provide an algorithm for computing a full characterization of possible initial values, classified in terms of the number of distinct formal power series solutions extending them. In addition, if a particular initial value is given, we present a second algorithm that computes all the formal power series solutions, up to a suitable degree, corresponding to it. Furthermore, when the ground field is the field of the complex numbers, we prove that the computed formal power series solutions are all convergent in suitable neighborhoods.
IoT Architectural Framework: Connection and Integration Framework for IoT Systems
Venue: arXiv cs.DC 2018
Year: 2018

The proliferation of the Internet of Things (IoT) has since seen a growing interest in architectural design and adaptive frameworks to promote the connection between heterogeneous IoT devices and IoT systems. The most widely favoured software architecture in IoT is the Service Oriented Architecture (SOA), which aims to provide a loosely coupled systems to leverage the use and reuse of IoT services at the middle-ware layer, to minimise system integration problems. However, despite the flexibility offered by SOA, the challenges of integrating, scaling and ensuring resilience in IoT systems persist. One of the key causes of poor integration in IoT systems is the lack of an intelligent, connection-aware framework to support interaction in IoT systems. This paper reviews existing architectural frameworks for integrating IoT devices and identifies the key areas that require further research improvements. The paper concludes by proposing a possible solution based on microservice. The proposed IoT integration framework benefits from an intelligent API layer that employs an external service assembler, service auditor, service monitor and service router component to coordinate service publishing, subscription, decoupling and service combination within the architecture.
A Design Space Exploration Methodology for Parameter Optimization in Multicore Processors
Venue: arXiv cs.DC 2018
Year: 2018

The need for application-specific design of multicore/manycore processing platforms is evident with computing systems finding use in diverse application domains. In order to tailor multicore/manycore processors for application specific requirements, a multitude of processor design parameters have to be tuned accordingly which involves rigorous and extensive design space exploration over large search spaces. In this paper, we propose an efficient methodology for design space exploration. We evaluate our methodology over two search spaces - small and large, using a cycle-accurate simulator (ESESC) and a standard set of PARSEC and SPLASH-2 benchmarks. For the smaller design space, we compare results obtained from our design space exploration methodology with results obtained from fully exhaustive search. The results show that solution quality obtained from our methodology are within 1.35% - 3.69% of the results obtained from fully exhaustive search while only exploring 2.74% - 3% of the design space. For larger design space, we compare solution quality of different results obtained by varying the number of tunable processor design parameters included in the exhaustive search phase of our methodology. The results show that including more number of tunable parameters in the exhaustive search phase of our methodology greatly improves solution quality.
Semi-BCI Algebras
Venue: arXiv cs.LO 2018
Year: 2018

The notion of semi-BCI algebras is introduced and some of its properties are investigated. This algebra is another generalization for BCI-algebras. It arises from the "intervalization" of BCI algebras. Semi-BCI have a similar structure to Pseudo-BCI algebras however they are not the same. In this paper we also provide an investigation on the similarity between these classes of algebras by showing how they relate to the process of intervalization.
3D Video Quality Assessment
Venue: arXiv cs.CV 2018
Year: 2018

A key factor in designing 3D systems is to understand how different visual cues and distortions affect the perceptual quality of 3D video. The ultimate way to assess video quality is through subjective tests. However, subjective evaluation is time consuming, expensive, and in most cases not even possible. An alternative solution is objective quality metrics, which attempt to model the Human Visual System (HVS) in order to assess the perceptual quality. The potential of 3D technology to significantly improve the immersiveness of video content has been hampered by the difficulty of objectively assessing Quality of Experience (QoE). A no-reference (NR) objective 3D quality metric, which could help determine capturing parameters and improve playback perceptual quality, would be welcomed by camera and display manufactures. Network providers would embrace a full-reference (FR) 3D quality metric, as they could use it to ensure efficient QoE-based resource management during compression and Quality of Service (QoS) during transmission.
A Learning-Based Visual Saliency Prediction Model for Stereoscopic 3D Video (LBVS-3D)
Venue: arXiv cs.CV 2018
Year: 2018

Over the past decade, many computational saliency prediction models have been proposed for 2D images and videos. Considering that the human visual system has evolved in a natural 3D environment, it is only natural to want to design visual attention models for 3D content. Existing monocular saliency models are not able to accurately predict the attentive regions when applied to 3D image/video content, as they do not incorporate depth information. This paper explores stereoscopic video saliency prediction by exploiting both low-level attributes such as brightness, color, texture, orientation, motion, and depth, as well as high-level cues such as face, person, vehicle, animal, text, and horizon. Our model starts with a rough segmentation and quantifies several intuitive observations such as the effects of visual discomfort level, depth abruptness, motion acceleration, elements of surprise, size and compactness of the salient regions, and emphasizing only a few salient objects in a scene. A new fovea-based model of spatial distance between the image regions is adopted for considering local and global feature calculations. To efficiently fuse the conspicuity maps generated by our method to one single saliency map that is highly correlated with the eye-fixation data, a random forest based algorithm is utilized. The performance of the proposed saliency model is evaluated against the results of an eye-tracking experiment, which involved 24 subjects and an in-house database of 61 captured stereoscopic videos. Our stereo video database as well as the eye-tracking data are publicly available along with this paper. Experiment results show that the proposed saliency prediction method achieves competitive performance compared to the state-of-the-art approaches.
Soft-Robust Actor-Critic Policy-Gradient
Venue: arXiv cs.LG 2018
Year: 2018

Robust Reinforcement Learning aims to derive optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the worst case scenario, robust policies can be overly conservative. Our soft-robust framework is an attempt to overcome this issue. In this paper, we present a novel Soft-Robust Actor-Critic algorithm (SR-AC). It learns an optimal policy with respect to a distribution over an uncertainty set and stays robust to model uncertainty but avoids the conservativeness of robust strategies. We show the convergence of SR-AC and test the efficiency of our approach on different domains by comparing it against regular learning methods and their robust formulations.
Onion-Peeling Outlier Detection in 2-D data Sets
Venue: arXiv cs.LG 2018
Year: 2018

Outlier Detection is a critical and cardinal research task due its array of applications in variety of domains ranging from data mining, clustering, statistical analysis, fraud detection, network intrusion detection and diagnosis of diseases etc. Over the last few decades, distance-based outlier detection algorithms have gained significant reputation as a viable alternative to the more traditional statistical approaches due to their scalable, non-parametric and simple implementation. In this paper, we present a modified onion peeling (Convex hull) genetic algorithm to detect outliers in a Gaussian 2-D point data set. We present three different scenarios of outlier detection using a) Euclidean Distance Metric b) Standardized Euclidean Distance Metric and c) Mahalanobis Distance Metric. Finally, we analyze the performance and evaluate the results.
An algorithm for hiding and recovering data using matrices
Venue: arXiv cs.CR 2018
Year: 2018

We present an algorithm for the recovery of a matrix $\mathbb{M}$ % (non-singular $\in $ $\mathbb{C}^{N\times N}$) by only being aware of two of its powers, $\mathbb{M}_{k_{1}}:=\mathbb{M}^{k_{1}}$ and $\mathbb{M}% _{k_{2}}:=\mathbb{M}^{k_{2}}$ ($k_{1}>k_{2}$) whose exponents are positive coprime numbers. The knowledge of the exponents is the key to retrieve matrix $\mathbb{M}$ out from the two matrices $\mathbb{M}_{k_{i}}$. The procedure combines products and inversions of matrices, and a few computational steps are needed to get $\mathbb{M}$, almost independently of the exponents magnitudes. Guessing the matrix $\mathbb{M}$ from the two matrices $\mathbb{M}_{k_{i}}$, without the knowledge of $k_{1}$ and $k_{2}$, is comparatively highly consuming in terms of number of operations. If a private message, contained in $\mathbb{M}$, has to be conveyed, the exponents can be encrypted and then distributed through a public key method as, for instance, the DF (Diffie-Hellman), the RSA (Rivest-Shamir-Adleman), or any other.
Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking
Venue: arXiv cs.CR 2018
Year: 2018

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.
Solving the Course-timetabling Problem of Cairo University Using Max-SAT
Venue: arXiv cs.AI 2018
Year: 2018

Due to the good performance of current SAT (satisfiability) and Max-SAT (maximum ssatisfiability) solvers, many real-life optimization problems such as scheduling can be solved by encoding them into Max-SAT. In this paper we tackle the course timetabling problem of the department of mathematics, Cairo University by encoding it into Max-SAT. Generating timetables for the department by hand has proven to be cumbersome and the generated timetable almost always contains conflicts. We show how the constraints can be modelled as a Max-SAT instance.
Android Inter-App Communication Threats, Solutions, and Challenges
Venue: arXiv cs.CR 2018
Year: 2018

Researchers and commercial companies have made a lot of efforts on detecting malware in Android platform. However, a recent malware threat, App collusion, makes malware detection challenging. In App collusion, two or more Apps collaborate to perform malicious actions by communicating with each other, which makes single App analysis insufficient. In this paper, we first introduce Android security mechanism and communication channels used by android Applications. Then we summarize the security vulnerabilities and potential threats introduced by App communication. Finally, we discuss state of art researches and challenges on App collusion detection.
A Multi-Modal Approach to Infer Image Affect
Venue: arXiv cs.CV 2018
Year: 2018

The group affect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. The state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. This paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the first time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.
Neural Lattice Language Models
Venue: arXiv cs.CL 2018
Year: 2018

In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions - including polysemy and existence of multi-word lexical items - into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95% relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94% relative to a character-level baseline.